Investigation of Machine Learning Approaches for Traumatic Brain Injury Classification via EEG Assessment in Mice.

Due to the difficulties and complications in the quantitative assessment of traumatic brain injury (TBI) and its increasing relevance in today's world, robust detection of TBI has become more significant than ever. In this work, we investigate several machine learning approaches to assess their performance in classifying electroencephalogram (EEG) data of TBI in a mouse model. Algorithms such as decision trees (DT), random forest (RF), neural network (NN), support vector machine (SVM), K-nearest neighbors (KNN) and convolutional neural network (CNN) were analyzed based on their performance to classify mild TBI (mTBI) data from those of the control group in wake stages for different epoch lengths. Average power in different frequency sub-bands and alpha:theta power ratio in EEG were used as input features for machine learning approaches. Results in this mouse model were promising, suggesting similar approaches may be applicable to detect TBI in humans in practical scenarios

Identifying safety strategies for on-farm grain bins using risk analysis

The potential for grain bin accidents exists each year on Arkansas farms and farms across the nation. The trend toward increasing utilization of on-farm grain drying and storage could lead to an increase in grain bin accidents. The sharp contrast between a safe, efficient operation and one that leads to injury or death can be represented as sets of farmer-decisions and subsequent chance events. A model was constructed to define the risk associated with grain bin entry and inbin activity so that safety interventions could be identified and implemented to reduce the probability of injury and death. A survey was distributed to Arkansas grain farmers to gather data on the level of safety education, storage techniques, operations management, and other parameters. The data collected from the survey provided quantitative input of many of the model’s probability-distribution functions. Using a fault tree (with parallel modes of failure) in conjunction with a Monte Carlo simulation technique, we evaluated six safety intervention strategies and identified the one with the greatest potential for reducing the risk of serous injury or death. As part of senior design in biological engineering, plans are underway to design and test a probe that can locate and break bridged grain (a common risk factor in grain bin management) while working outside the bin on the ground

Matching pursuit-based compressive sensing in a wearable biomedical accelerometer fall diagnosis device

There is a significant high fall risk population, where individuals are susceptible to frequent falls and obtaining significant injury, where quick medical response and fall information are critical to providing efficient aid. This article presents an evaluation of compressive sensing techniques in an accelerometer-based intelligent fall detection system modelled on a wearable Shimmer biomedical embedded computing device with Matlab. The presented fall detection system utilises a database of fall and activities of daily living signals evaluated with discrete wavelet transforms and principal component analysis to obtain binary tree classifiers for fall evaluation. 14 test subjects undertook various fall and activities of daily living experiments with a Shimmer device to generate data for principal component analysis-based fall classifiers and evaluate the proposed fall analysis system. The presented system obtains highly accurate fall detection results, demonstrating significant advantages in comparison with the thresholding method presented. Additionally, the presented approach offers advantageous fall diagnostic information. Furthermore, transmitted data accounts for over 80% battery current usage of the Shimmer device, hence it is critical the acceleration data is reduced to increase transmission efficiency and in-turn improve battery usage performance. Various Matching pursuit-based compressive sensing techniques have been utilised to significantly reduce acceleration information required for transmission.Scopu

An ergonomics analysis of manual versus chainsaw high ladder pruning of Pinus radiata in New Zealand : a thesis in partial fulfilment of the requirements for the degree of Master of Philosophy at Massey University

Two methods of ladder pruning Pinus radiata from 4.5 - 6.0 metres were compared using a cost-benefit approach within a framework provided by ergonomics. Chainsaw pruning is practiced in areas of New Zealand where large branches occur. The objectives of the research were to compare the costs and benefits of the two pruning techniques and provide recommendations as to whether or not the practice of chainsaw pruning should continue. These objectives were achieved by comparing the risk of injury, the physiological costs, the musculoskeletal costs, the productivity and the quality associated with the use of the two techniques. The general methods used to assess the relative costs and benefits of the two techniques were: l. Numeric descriptions of the 'risk' involved with each method of pruning 2. The use of a relative heart rate index to compare the physiological cost of the two techniques 3. Using questionnaires focusing on musculoskeletal pain and discomfort to assess any relative differences between the two techniques 4. Using continuous time study to quantify any difference in labour productivity between the two techniques 5. Sampling pruned trees to assess differences in the quality of work between manual and chainsaw pruning The research concludes that although both methods of pruning are hazardous, chainsaw pruning is more hazardous than manual pruning. Chainsaw and manual pruning were found to have the same physiological costs. Findings of the research indicate that manual pruning is not associated with a higher prevalence of musculoskeletal discomfort than chainsaw pruning on a yearly basis, although it is associated with a greater relative increase in BPD on a day to day basis and that this may lead to the development of musculoskeletal disease. Chainsaw pruning was found to be significantly more productive than manual pruning, although this was at the cost of quality. The research concludes by recommending that the use of chainsaw pruning should be limited to areas where the branches are demonstrably large. Further research is called for to compare the physiological and musculoskeletal costs of manual pruning in plantation areas of both large and small branch sizes. Further research is called for to compare the safety of two methods of chainsaw pruning with the use of the technique of wrapping one leg around the tree as opposed to not wrapping the leg around the tree. Research to investigate new ladder designs which are safer to use in the New Zealand forest environment is also called for

Dynamic low-level context for the detection of mild traumatic brain injury.

Mild traumatic brain injury (mTBI) appears as low contrast lesions in magnetic resonance (MR) imaging. Standard automated detection approaches cannot detect the subtle changes caused by the lesions. The use of context has become integral for the detection of low contrast objects in images. Context is any information that can be used for object detection but is not directly due to the physical appearance of an object in an image. In this paper, new low-level static and dynamic context features are proposed and integrated into a discriminative voxel-level classifier to improve the detection of mTBI lesions. Visual features, including multiple texture measures, are used to give an initial estimate of a lesion. From the initial estimate novel proximity and directional distance, contextual features are calculated and used as features for another classifier. This feature takes advantage of spatial information given by the initial lesion estimate using only the visual features. Dynamic context is captured by the proposed posterior marginal edge distance context feature, which measures the distance from a hard estimate of the lesion at a previous time point. The approach is validated on a temporal mTBI rat model dataset and shown to have improved dice score and convergence compared to other state-of-the-art approaches. Analysis of feature importance and versatility of the approach on other datasets are also provided

A Predictive Model for Assessment of Successful Outcome in Posterior Spinal Fusion Surgery

Background: Low back pain is a common problem in many people. Neurosurgeons recommend posterior spinal fusion (PSF) surgery as one of the therapeutic strategies to the patients with low back pain. Due to the high risk of this type of surgery and the critical importance of making the right decision, accurate prediction of the surgical outcome is one of the main concerns for the neurosurgeons.Methods: In this study, 12 types of multi-layer perceptron (MLP) networks and 66 radial basis function (RBF) networks as the types of artificial neural network methods and a logistic regression (LR) model created and compared to predict the satisfaction with PSF surgery as one of the most well-known spinal surgeries.Results: The most important clinical and radiologic features as twenty-seven factors for 480 patients (150 males, 330 females; mean age 52.32 ± 8.39 years) were considered as the model inputs that included: age, sex, type of disorder, duration of symptoms, job, walking distance without pain (WDP), walking distance without sensory (WDS) disorders, visual analog scale (VAS) scores, Japanese Orthopaedic Association (JOA) score, diabetes, smoking, knee pain (KP), pelvic pain (PP), osteoporosis, spinal deformity and etc. The indexes such as receiver operating characteristic–area under curve (ROC-AUC), positive predictive value, negative predictive value and accuracy calculated to determine the best model. Postsurgical satisfaction was 77.5% at 6 months follow-up. The patients divided into the training, testing, and validation data sets.Conclusion: The findings showed that the MLP model performed better in comparison with RBF and LR models for prediction of PSF surgery.Keywords: Posterior spinal fusion surgery (PSF); Prediction, Surgical satisfaction; Multi-layer perceptron (MLP); Logistic regression (LR) (PDF) A Predictive Model for Assessment of Successful Outcome in Posterior Spinal Fusion Surgery. Available from: https://www.researchgate.net/publication/325679954_A_Predictive_Model_for_Assessment_of_Successful_Outcome_in_Posterior_Spinal_Fusion_Surgery [accessed Jul 11 2019].Peer reviewe

Neuroimaging of structural pathology and connectomics in traumatic brain injury: Toward personalized outcome prediction.

Recent contributions to the body of knowledge on traumatic brain injury (TBI) favor the view that multimodal neuroimaging using structural and functional magnetic resonance imaging (MRI and fMRI, respectively) as well as diffusion tensor imaging (DTI) has excellent potential to identify novel biomarkers and predictors of TBI outcome. This is particularly the case when such methods are appropriately combined with volumetric/morphometric analysis of brain structures and with the exploration of TBI-related changes in brain network properties at the level of the connectome. In this context, our present review summarizes recent developments on the roles of these two techniques in the search for novel structural neuroimaging biomarkers that have TBI outcome prognostication value. The themes being explored cover notable trends in this area of research, including (1) the role of advanced MRI processing methods in the analysis of structural pathology, (2) the use of brain connectomics and network analysis to identify outcome biomarkers, and (3) the application of multivariate statistics to predict outcome using neuroimaging metrics. The goal of the review is to draw the community's attention to these recent advances on TBI outcome prediction methods and to encourage the development of new methodologies whereby structural neuroimaging can be used to identify biomarkers of TBI outcome

Prediction of survival probabilities with Bayesian Decision Trees

Practitioners use Trauma and Injury Severity Score (TRISS) models for predicting the survival probability of an injured patient. The accuracy of TRISS predictions is acceptable for patients with up to three typical injuries, but unacceptable for patients with a larger number of injuries or with atypical injuries. Based on a regression model, the TRISS methodology does not provide the predictive density required for accurate assessment of risk. Moreover, the regression model is difficult to interpret. We therefore consider Bayesian inference for estimating the predictive distribution of survival. The inference is based on decision tree models which recursively split data along explanatory variables, and so practitioners can understand these models. We propose the Bayesian method for estimating the predictive density and show that it outperforms the TRISS method in terms of both goodness-of-fit and classification accuracy. The developed method has been made available for evaluation purposes as a stand-alone application